'''simple docstring'''




import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def     __snake_case			(							lowerCAmelCase							:  int   ,	lowerCAmelCase							:  Any   ,	lowerCAmelCase							:  Tuple   ,	lowerCAmelCase							:  Union[str, Any]      ):



 if isinstance(lowerCAmelCase   ,	lowerCAmelCase      ):
  __UpperCAmelCase								=       np.full((len(lowerCAmelCase      ), sequence_length, 2)   ,	lowerCAmelCase      )
 else:
  __UpperCAmelCase								=       np.full((len(lowerCAmelCase      ), sequence_length)   ,	lowerCAmelCase      )

 for i, tensor in enumerate(lowerCAmelCase      ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase   ,	lowerCAmelCase      ):
    __UpperCAmelCase								=       tensor[:sequence_length]
   else:
    __UpperCAmelCase								=       tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase   ,	lowerCAmelCase      ):
    __UpperCAmelCase								=       tensor[:sequence_length]
   else:
    __UpperCAmelCase								=       tensor[:sequence_length]

 return out_tensor.tolist()



def     __snake_case			(							lowerCAmelCase							:  Dict      ):
 __UpperCAmelCase								=       ord(lowerCAmelCase      )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 __UpperCAmelCase								=       unicodedata.category(lowerCAmelCase      )
 if cat.startswith('P'      ):
  return True
 return False

@dataclass
class 	_lowercase(  _UpperCAmelCase					):



   """simple docstring"""





   __lowerCamelCase     =	42
   __lowerCamelCase     =	True
   __lowerCamelCase     =	None
   __lowerCamelCase     =	None
   __lowerCamelCase     =	-100
   __lowerCamelCase     =	"pt"





   def       snake_case						(				self:     Optional[int]      ,a:     List[str]						):
    import torch

    __UpperCAmelCase								=       'label' if 'label' in features[0].keys() else 'labels'
    __UpperCAmelCase								=       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __UpperCAmelCase								=       self.tokenizer.pad(
        lowerCAmelCase__      ,padding=self.padding      ,max_length=self.max_length      ,pad_to_multiple_of=self.pad_to_multiple_of      ,return_tensors='pt' if labels is None else None      ,)

    if labels is None:
     return batch

    __UpperCAmelCase								=       torch.tensor(batch['entity_ids']						).shape[1]
    __UpperCAmelCase								=       self.tokenizer.padding_side
    if padding_side == "right":
     __UpperCAmelCase								=       [
         list(lowerCAmelCase__						) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__						)) for label in labels
     ]
    else:
     __UpperCAmelCase								=       [
         [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__						)) + list(lowerCAmelCase__						) for label in labels
     ]

    __UpperCAmelCase								=       [feature['ner_tags'] for feature in features]
    __UpperCAmelCase								=       padding_tensor(lowerCAmelCase__      ,-1      ,lowerCAmelCase__      ,lowerCAmelCase__						)
    __UpperCAmelCase								=       [feature['original_entity_spans'] for feature in features]
    __UpperCAmelCase								=       padding_tensor(lowerCAmelCase__      ,(-1, -1)      ,lowerCAmelCase__      ,lowerCAmelCase__						)
    __UpperCAmelCase								=       {k: torch.tensor(lowerCAmelCase__      ,dtype=torch.intaa						) for k, v in batch.items()}

    return batch




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')